advanced weather prediction oct 01

8/11/2019 Advanced Weather Prediction Oct 01

1/21

Center for Atmospheric Physics

Adaptive Observation Strategiesfor Advanced Weather Prediction

David P. Bacon, Zafer Boybeyi


Science Applications International Corporation

Michael Kaplan

Dept. of Marine, Earth, & Atmos. Sci.

North Carolina State University

October 30, 2001

David P. Bacon

(703)676-4594

[email protected]


2/21


Weather Forecasting A Primer

Weather forecasting relies on a system of systems:

In-situobservations (surface, balloon, & aircraft) Remotely-sensed observations (satellite-based)

Data assimilation (creating a physically consistent 3-D dataset)

Prognostic models (extrapolation into 4-D)


3/21


In-situObservations

Irregular spatial distribution

Where the people (and planes) are

Quasi-regular temporal distribution

Surface observations

Hourly with special reportsfor signif icant weather

Balloon observations

Twice-daily at 0000Z & 1200Z

Aircraft observations

Regular intervals


4/21


Remote Sensing Observation Strategies

The beginning:

TIROS-1 (April 1, 1960)

Television and InfraRed Operational Satellite

ATS-1 (December 6, 1966)

Applications Technology Satellite Geostationary

Communications system test bed

Raster based systems Vidicon tubes to CCDs


5/21


Data Assimilation

Goal: A physically consistent 3-D representationof the atmosphere at an instant of time

Requirements: Rationalize a disparate set of data that is amix of irregular and grid point information atsynoptic (0000Z & 1200Z) and asynoptic times

Modify large-scale effects to reflect small-scale features

Capture steep gradients critical to severe weather


6/21


Prognostic Models

Most models are based on a rectangular grid structure

Intuitively obvious Simplest tiling

Simple operator decomposition

Higher resolution is obtained by nesting

Conceptually easy

Numerically tricky

Reflective internal boundaries

Differing surface conditions Scale interactive information exchange

Initial conditions obtained from data assimilation system

Generally at synoptic times


7/21


A Vicious Circle

Increased horizontal () and vertical () resolution in models leads to:

Higher resolution initial conditions: 2

Increased run-time: 2

Increased output volume: 2 max(, , , , )

Higher resolution ICs requires higher resolution observations


8/21


Dynamic Adaptation of Data Assimilation

and Model Resolution

Initial grid for an

OMEGA simulation ofHurricane Floyd andthe grid (inset) at 72hours


9/21


OMEGA Forecast of Hurricane Floyd


10/21


Control Simulation

Initialization:

1200Z September 13

NOGAPS Analysis Boundary Conditions:

NOGAPS Forecast

Grid Resolution:

5 - 15 km


11/21


Verification of Control Simulation

Storm Tracks White: Observations

Red: Control


12/21


Coarse Resolution Simulation

Initialization:

0000Z September 14

NOGAPS Analysis Boundary Conditions:

NOGAPS Forecast

Grid Resolution:

75 - 120 km


13/21


Verification of Coarse Resolution Simulation

Storm Tracks

White: Observations

Red: Control Yellow: Coarse Res

Significant westward track

deviation


14/21


15/21


Case #1: 654 Targeted Observations

Coarse Resolution

Configuration Added 654 observations

All cell centroids in boxaround storm


16/21


Verification of Case #1 (654 Targeted Observations)

Storm Tracks

White: Observations

Red: Control

Yellow: Coarse Res

Orange: Case #1 (654)

Noticeable improvement inforecasted track


17/21



Coarse ResolutionConfiguration

Added 100 observations

Regular 10 x 10 arrayaround storm

Storm Tracks

White: Observations Red: Control

Yellow: Coarse Res

Orange: Case #1 (654)

Green: Case #2 (100)

Virtually identical track toCase #1 with only 20% of

the targeted observations


18/21




Added 11 observations

Around initial stormlocation

Storm Tracks

White: Observations

Red: Control

Yellow: Coarse Res

Cyan: Case #3 (11)

Very minor difference fromCoarse resolution simulation


19/21




Added 50 observations Along forecasted track

Storm Tracks

White: Observations

Red: Control

Yellow: Coarse Res

Pink: Case #4 (50)

While this case improved theforecast early on, it did notimprove the track at latertimes as much as Case #1 or

Case #2


20/21



Coarse Resolution

Configuration Added 50 observations

Along forecasted track

Storm Tracks

White: Observations

Red: Control

Yellow: Coarse Res

Cyan: Case #5 (25)

Track nearly identical toCase #4.


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Conclusions and Ramifications

Targeted observations can have a dramatic impact on storm forecasts

Improvement in initial storm conditions has the largest payoff

Other scenarios may have different requirements

Greatest improvement will come in identifying and obtaining keyobservations for developingconvective storms

The critical scales are so small and hence the volume of regulararrays of observations is so large that either the communicationsbecome a dominant problem orthe extraction of a signal fromthe noise of data bits prevents utilization

The routine utilization of targeted as opposed to general satellite

observations has significant impact on satellite operations

Timeliness is key Communication & data mining issues

A tightly linked forecast and observational system can address theseissues as well

advanced weather prediction oct 01

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